Change-Related Communication and Employees' Responses During the Anticipation Stage of IT-Enabled Organizational Transformation
Bibliographic record
Abstract
This study focuses on a medium-sized, nonprofit healthcare service management organization which was undergoing a major transformation enabled by information technology. We examined how uncertainty during the anticipation stage affected the staff's emotional responses to the new technology. We categorized employees' understandings of the new technology into four domains: (1) why the technology was adopted, (2) what the functionality of the technology would be, (3) how the technology might affect their work life, and (4) when such an effect would materialize. Due to uncertainty during the anticipation stage, participants were not able to fully appraise the situation. Based on their hypothetical expectations, participants experienced both hope and fear (i.e., suspense). In order to manage the psychological discomfort created by this emotional ambivalence, participants actively sought social interaction with colleagues in order to gain information about the new technology, to build camaraderie, or both. The former directly decreased the level of perceived uncertainty by closing information gaps, and the latter reduced anxiety by creating a sense of community. Our study illustrates how seeking social support during the pre-implementation time frame has the capacity to help employees prepare themselves, both cognitively and emotionally, for adopting a new technology before they have any tangible interaction with it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".